Dear,
A complete scoring system also requires combining internal rating determination, like described in the Chapter I of this post, with evaluations from external sources.
These external sources include Credit Rating and Scoring Agencies and analysis of the Capital Markets behavior.
Credit Risk managers use two main approaches for credit risk modelling based on Capital Markets data analysis; Structural and Reduced models.
- Structural models, like Merton, are used for calculating probabilities of default focusing on equity prices.
- Reduced models, like Jarrow-Turnbull, focus on debt values, and perform the analysis looking at evolution of interest rates.
Obviously they also use combinations or evolutions of the above on their internal models. For instance, some months ago I had a conversation with the VP responsible of Risk Management in an investment bank in Singapore. He explained me some interesting details of their rating models and particularly, how they included the current, future and volatility prices of some Financial Instruments in their model for estimating the solvency of governments and corporate
counterparties.
Those models are not directly supported by SAP tools, but it could be done by complementing SAP Components (Bank Analyzer, Market Risk Analyzer, etc) with external statistical analysis tools (SPSS). It’s not a standard construction, but in my opinion it’s worth exploring it during the project preparation.
Finally, managing ratings requires more than effective determination of the Business Partner Risk, it also requires centralized management of the scoring data and seamless integration with the Operational Systems. Not having centralized controls is a big weakness in a Credit Risk System, as it can be the root cause of erroneous decisions and consequently wasting Capital.
For instance; without a centralized credit control, a Business Partner, considered insolvent in one country or by a business line, would be able of over-passing his credit limit by borrowing funds in another. A well known example is how the Greek government could hide part of its deficit, getting funds by using derivatives contracts with Goldman Sachs support.
http://www.spiegel.de/international/europe/greek-debt-crisis-how-goldman-sachs-helped-greece-to-mask-its-true-debt-a-676634.html
The Credit Risk component of SAP combines the capacity of acting as central repository of counterparty risk data, with the flexibility of adapting the risk data to different business scenarios, by storing it in separated credit segment data. This segment data could be tailored to the requirements of specific business lines or jurisdictions, and then be delivered this way to separated business areas or legal entities of the Financial Group (Insurance, Leasing, Retail and Private Banking, etc.).
As it happens with other SAP Banking solutions, some of the components described in this post are not very well known in the market; the Historical Database of Basel II has been implemented in several banks in Asia Pacific and Africa and a good number of European Banks, but I’m not aware of any implementation in the America region and it’s even more difficult to find references of the described integrated scenarios.
On the other hand, the advantages of using an integrated platform for the management of credit risk are abundant, rating calculation and risk management require effective calculation and centralized control, but also common semantics that helps Banks’ executives to understand
the root cause of their risk exposures, from the operational to the analytical level and vice-versa.
That’s SAP value proposition; common semantics and seamless integration of software components. From this perspective I don’t have doubts that as soon as we improve the on field knowledge of SAP Banking capabilities on Credit Risk management, it will become the market leader.
K. Regards,
Ferran.
These external sources include Credit Rating and Scoring Agencies and analysis of the Capital Markets behavior.
Credit Risk managers use two main approaches for credit risk modelling based on Capital Markets data analysis; Structural and Reduced models.
- Structural models, like Merton, are used for calculating probabilities of default focusing on equity prices.
- Reduced models, like Jarrow-Turnbull, focus on debt values, and perform the analysis looking at evolution of interest rates.
Obviously they also use combinations or evolutions of the above on their internal models. For instance, some months ago I had a conversation with the VP responsible of Risk Management in an investment bank in Singapore. He explained me some interesting details of their rating models and particularly, how they included the current, future and volatility prices of some Financial Instruments in their model for estimating the solvency of governments and corporate
counterparties.
Those models are not directly supported by SAP tools, but it could be done by complementing SAP Components (Bank Analyzer, Market Risk Analyzer, etc) with external statistical analysis tools (SPSS). It’s not a standard construction, but in my opinion it’s worth exploring it during the project preparation.
Finally, managing ratings requires more than effective determination of the Business Partner Risk, it also requires centralized management of the scoring data and seamless integration with the Operational Systems. Not having centralized controls is a big weakness in a Credit Risk System, as it can be the root cause of erroneous decisions and consequently wasting Capital.
For instance; without a centralized credit control, a Business Partner, considered insolvent in one country or by a business line, would be able of over-passing his credit limit by borrowing funds in another. A well known example is how the Greek government could hide part of its deficit, getting funds by using derivatives contracts with Goldman Sachs support.
http://www.spiegel.de/international/europe/greek-debt-crisis-how-goldman-sachs-helped-greece-to-mask-its-true-debt-a-676634.html
The Credit Risk component of SAP combines the capacity of acting as central repository of counterparty risk data, with the flexibility of adapting the risk data to different business scenarios, by storing it in separated credit segment data. This segment data could be tailored to the requirements of specific business lines or jurisdictions, and then be delivered this way to separated business areas or legal entities of the Financial Group (Insurance, Leasing, Retail and Private Banking, etc.).
As it happens with other SAP Banking solutions, some of the components described in this post are not very well known in the market; the Historical Database of Basel II has been implemented in several banks in Asia Pacific and Africa and a good number of European Banks, but I’m not aware of any implementation in the America region and it’s even more difficult to find references of the described integrated scenarios.
On the other hand, the advantages of using an integrated platform for the management of credit risk are abundant, rating calculation and risk management require effective calculation and centralized control, but also common semantics that helps Banks’ executives to understand
the root cause of their risk exposures, from the operational to the analytical level and vice-versa.
That’s SAP value proposition; common semantics and seamless integration of software components. From this perspective I don’t have doubts that as soon as we improve the on field knowledge of SAP Banking capabilities on Credit Risk management, it will become the market leader.
K. Regards,
Ferran.
1 comment:
Hi Ferran,
Facing an interesting issue in bank nranch cash management. Just wanted to bring the detail on this forum.
While implementing a complete SAP solution for banking i came across an interesting issue related with branch cash management which is difficult to be solved with standard SAP solutions. My client (a commercial bank) is having cash warehouse account (Overall cash in hand for bank) with ERP system & branch cash accounts in sap transactional banking system. Now when the amount will be sent from cash warehouse to branch cash accounts to meet daily operations (ex. Daily cash transactions, adverse clearing etc.) the accounting will be done as follows :
In ERP : In Banking services :
Dr.. Cash in transit (Comes In) Dr. Branch Cash (Comes In)
Cr. Cash warehouse (Goes Out) Cr. Cash in Transit (Goes Out)
Now ERP transaction will hit the GL directly in SAP FI (Accounting system) while transactions in Banking services will hit the GL via SAP bank analyzer (AFI) where all cash accounts maintained in operational system will be replicated as financial transactions. So ultimately at GL level the accounting entries will be reconciled.
But actual problem starts from here. We have a negative entry in cash in transit is standing in banking services which is reflected despite at GL level it got reconciled since this account is mirror of cash in transit maintained at ERP level. There is no standard cash management system existing in the architecture. Now the issue is client doesn’t want a negative entry standing at branch & they are expecting this to be zero with some automated process where it get reconciled with corresponding ERP transaction at branch level & not only at GL level.
I was just wondering if someone has faced similar issue & if there is any standard solution in sap for this or we need to maintain some custom interface to reconcile the entries between ERP & Banking Services as manual report is not accepted by the client & they want some automated process for this.
Regards,
Anish
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